Fish_Stuff
Callie Stephenson
2024-03-28
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)TIDY DATA:
First chunk of code subsets to only using 8 blocks per survey location, 4 from an ‘on’ and 4 from an ‘adjacent’. I also remove pins where camera malfunctions made this not possible (Pin 13 and Pin 2).
Since we could count some species as distinct individuals by life stages, I have combined those counts together here:
And built a little code to double check that it went correctly (should create two empty data frames)
I then deal with extreme schooling events following methods from the Donovan regimes paper: “Additional methodology was developed for dealing with outliers in the fish data, accounting for extreme observations of schooling species. Extreme observations in the database were defined by calculating the upper 99.9% of all individual observations (e.g. one species, size and count on an individual transect), resulting in 26 observations out of over 0.5 million, comprised of 11 species. The distribution of individual counts in the entire database for those 11 species was then used to identify observations that fell above the 99.0% quantile of counts for each species individually. These observations were adjusted to the 99.0% quantile for analysis.”
and ending by renaming the dataframe as just fish_tidy for future use: this allows me to make extra tidy data adjustments without breaking the dataframe name later on in the code!
Data Vis
Linda from Johanssen Lab reccommended to make a plot that is ordered by the most abundant fish near the seep and look for any patterns, so I went and made this plot a number of ways. First, I kept all the species and looked a just the average abundance at all pins, to see if the communities looked relatively similar in this way.
All species average abundance across gradient
This is too busy to read, and no major patterns jump out. I subset this to just the top 20 species along the gradient, and again the order of the pins does not have biological relevance.
Most common 20 species average abundance across gradient
So, now I will order the pins by those most common at pin 14
This isn’t showing the most obvious patterns, but there were only a few species of fish that swam in the sane near the seep. So, I’ll reorder the pins by the distance to the seep
Most common species average abundance at each pin by distance to seep:
To see if maybe a nutrient parameter would be good descriptor, I do this also by the low tide mean silicate
Most common species average abundance across the gradient of low tide mean silicate
Most common species average abundance across the gradient of nutrient delivery
Nutrient delivery quantified as a pc axis I got from the low tide mean of major nutrients across the gradient.This axis captures 72.87298% of the variation in mean low tide TA, Phosphate, Silicate, and N + N. Higer numbers on this axis indicate higher values of these variables.
Alpha Diversity:
Calculating richness against gradient metrics:
Calculating diversity against gradient metrics:
NMDS Non-Dimensional Ordination
Code from Linda:
I’ve been going back and forth on the transformation here, and am currently going with hellinger instead of vegan’s default, the wisconsin. This is because, from my limited understanding, the hellinger better handles cases where species are not present in multiple sites (known as double zeros).
## Run 0 stress 0.2181495
## Run 1 stress 0.2240668
## Run 2 stress 0.2292528
## Run 3 stress 0.215009
## ... New best solution
## ... Procrustes: rmse 0.0245656 max resid 0.179733
## Run 4 stress 0.2169562
## Run 5 stress 0.223723
## Run 6 stress 0.2266768
## Run 7 stress 0.2230438
## Run 8 stress 0.2147991
## ... New best solution
## ... Procrustes: rmse 0.02064715 max resid 0.1660749
## Run 9 stress 0.2249218
## Run 10 stress 0.2212121
## Run 11 stress 0.216176
## Run 12 stress 0.2206038
## Run 13 stress 0.2226128
## Run 14 stress 0.2164359
## Run 15 stress 0.2246726
## Run 16 stress 0.2293422
## Run 17 stress 0.2244754
## Run 18 stress 0.2180973
## Run 19 stress 0.2215975
## Run 20 stress 0.2200863
## Run 21 stress 0.2227886
## Run 22 stress 0.2264458
## Run 23 stress 0.2148052
## ... Procrustes: rmse 0.009386133 max resid 0.08742263
## Run 24 stress 0.2161498
## Run 25 stress 0.2144762
## ... New best solution
## ... Procrustes: rmse 0.01929883 max resid 0.1668579
## Run 26 stress 0.2238373
## Run 27 stress 0.2175369
## Run 28 stress 0.2157275
## Run 29 stress 0.2234851
## Run 30 stress 0.2266754
## Run 31 stress 0.2299296
## Run 32 stress 0.2316667
## Run 33 stress 0.2196773
## Run 34 stress 0.2252596
## Run 35 stress 0.2263761
## Run 36 stress 0.2234551
## Run 37 stress 0.2147983
## ... Procrustes: rmse 0.01918736 max resid 0.1671084
## Run 38 stress 0.2261841
## Run 39 stress 0.22
## Run 40 stress 0.2229114
## Run 41 stress 0.2164859
## Run 42 stress 0.2217249
## Run 43 stress 0.2274
## Run 44 stress 0.2278007
## Run 45 stress 0.2149809
## Run 46 stress 0.2277742
## Run 47 stress 0.2307192
## Run 48 stress 0.2147482
## ... Procrustes: rmse 0.02076593 max resid 0.1673905
## Run 49 stress 0.223277
## Run 50 stress 0.2221087
## Run 51 stress 0.2140254
## ... New best solution
## ... Procrustes: rmse 0.0224709 max resid 0.1675455
## Run 52 stress 0.2250928
## Run 53 stress 0.2147524
## Run 54 stress 0.2177938
## Run 55 stress 0.2181147
## Run 56 stress 0.2260823
## Run 57 stress 0.2247112
## Run 58 stress 0.2277288
## Run 59 stress 0.2213905
## Run 60 stress 0.2144409
## ... Procrustes: rmse 0.02351755 max resid 0.1671795
## Run 61 stress 0.2275651
## Run 62 stress 0.2188578
## Run 63 stress 0.2220112
## Run 64 stress 0.2169548
## Run 65 stress 0.221224
## Run 66 stress 0.2301647
## Run 67 stress 0.2199055
## Run 68 stress 0.2175418
## Run 69 stress 0.228291
## Run 70 stress 0.2178267
## Run 71 stress 0.2341052
## Run 72 stress 0.2245736
## Run 73 stress 0.2248075
## Run 74 stress 0.223997
## Run 75 stress 0.2255935
## Run 76 stress 0.2145763
## Run 77 stress 0.2184732
## Run 78 stress 0.2149975
## Run 79 stress 0.2240143
## Run 80 stress 0.224853
## Run 81 stress 0.213289
## ... New best solution
## ... Procrustes: rmse 0.01752736 max resid 0.170708
## Run 82 stress 0.2198037
## Run 83 stress 0.2138676
## Run 84 stress 0.2131262
## ... New best solution
## ... Procrustes: rmse 0.009064877 max resid 0.0853702
## Run 85 stress 0.2174977
## Run 86 stress 0.2189414
## Run 87 stress 0.2228977
## Run 88 stress 0.226154
## Run 89 stress 0.2234279
## Run 90 stress 0.230438
## Run 91 stress 0.2153189
## Run 92 stress 0.2245302
## Run 93 stress 0.231548
## Run 94 stress 0.2147984
## Run 95 stress 0.2134637
## ... Procrustes: rmse 0.01540583 max resid 0.09271176
## Run 96 stress 0.2234696
## Run 97 stress 0.2337208
## Run 98 stress 0.2218073
## Run 99 stress 0.2271382
## Run 100 stress 0.2144068
## Run 101 stress 0.2161294
## Run 102 stress 0.2279404
## Run 103 stress 0.2259604
## Run 104 stress 0.2264587
## Run 105 stress 0.2132355
## ... Procrustes: rmse 0.00758539 max resid 0.0851397
## Run 106 stress 0.2148231
## Run 107 stress 0.2185818
## Run 108 stress 0.2273331
## Run 109 stress 0.2216843
## Run 110 stress 0.2193795
## Run 111 stress 0.2165178
## Run 112 stress 0.2220172
## Run 113 stress 0.2210423
## Run 114 stress 0.2227158
## Run 115 stress 0.2237968
## Run 116 stress 0.2207868
## Run 117 stress 0.2215269
## Run 118 stress 0.2254178
## Run 119 stress 0.2210594
## Run 120 stress 0.2217696
## Run 121 stress 0.2268071
## Run 122 stress 0.2182462
## Run 123 stress 0.2147519
## Run 124 stress 0.219702
## Run 125 stress 0.2333215
## Run 126 stress 0.2205612
## Run 127 stress 0.2289892
## Run 128 stress 0.2138569
## Run 129 stress 0.2155727
## Run 130 stress 0.2139119
## Run 131 stress 0.2203901
## Run 132 stress 0.2148622
## Run 133 stress 0.2163937
## Run 134 stress 0.2142073
## Run 135 stress 0.226597
## Run 136 stress 0.2291944
## Run 137 stress 0.2167617
## Run 138 stress 0.2242512
## Run 139 stress 0.2131061
## ... New best solution
## ... Procrustes: rmse 0.00155974 max resid 0.01510734
## Run 140 stress 0.2224013
## Run 141 stress 0.2290244
## Run 142 stress 0.2301673
## Run 143 stress 0.2210137
## Run 144 stress 0.2238809
## Run 145 stress 0.2138329
## Run 146 stress 0.2211195
## Run 147 stress 0.2221796
## Run 148 stress 0.2247256
## Run 149 stress 0.223097
## Run 150 stress 0.213305
## ... Procrustes: rmse 0.01019956 max resid 0.09195572
## Run 151 stress 0.2233642
## Run 152 stress 0.2258063
## Run 153 stress 0.2210482
## Run 154 stress 0.2235614
## Run 155 stress 0.2328143
## Run 156 stress 0.2214344
## Run 157 stress 0.2245912
## Run 158 stress 0.2212461
## Run 159 stress 0.2143121
## Run 160 stress 0.2231074
## Run 161 stress 0.220895
## Run 162 stress 0.2131264
## ... Procrustes: rmse 0.001494647 max resid 0.01469812
## Run 163 stress 0.22485
## Run 164 stress 0.2223159
## Run 165 stress 0.2208288
## Run 166 stress 0.2210143
## Run 167 stress 0.2248444
## Run 168 stress 0.2151182
## Run 169 stress 0.2159007
## Run 170 stress 0.2195229
## Run 171 stress 0.2301863
## Run 172 stress 0.221446
## Run 173 stress 0.2230421
## Run 174 stress 0.2262756
## Run 175 stress 0.2240575
## Run 176 stress 0.220404
## Run 177 stress 0.2260385
## Run 178 stress 0.2131125
## ... Procrustes: rmse 0.001154061 max resid 0.006556767
## ... Similar to previous best
## *** Best solution repeated 1 times
##
## Call:
## metaMDS(comm = mat.dis_fish, distance = "bray", k = 2, trymax = 500)
##
## global Multidimensional Scaling using monoMDS
##
## Data: mat.dis_fish
## Distance: bray
##
## Dimensions: 2
## Stress: 0.2131061
## Stress type 1, weak ties
## Best solution was repeated 1 time in 178 tries
## The best solution was from try 139 (random start)
## Scaling: centring, PC rotation, halfchange scaling
## Species: scores missing
## NMDS1 NMDS2 Site
## 1 -0.01994678 -0.10271108 1
## 2 -0.27652909 -0.16221078 1
## 3 0.18554752 -0.33247178 1
## 4 -0.34959467 -0.02655977 1
## 5 0.19632070 -0.12327810 1
## 6 -0.27259959 -0.38027066 1## $vectors
## NMDS1 NMDS2 r2 Pr(>r)
## Acanthurus nigrofuscus -0.89107 0.45386 0.0496 0.032 *
## Caranx melampygus 0.71077 0.70342 0.0028 0.828
## Chaetodon vagabundus 0.98387 0.17887 0.2544 0.001 ***
## Chlorurus spilurus -0.90096 0.43391 0.0056 0.618
## Gomphosus varius -0.36769 -0.92995 0.1812 0.001 ***
## Halichoeres trimaculatus -0.03949 0.99922 0.2430 0.001 ***
## Mulloidichthys flavolineatus 0.17929 0.98380 0.0190 0.211
## Parupeneus multifasciatus -0.99987 0.01633 0.0201 0.233
## Rhinecanthus aculeatus -0.25063 0.96808 0.2340 0.001 ***
## Stegastes sp -0.38919 -0.92116 0.4279 0.001 ***
## Stethojulis bandanensis 0.00951 0.99995 0.2337 0.001 ***
## Thalassoma hardwicke -0.68901 -0.72475 0.0987 0.002 **
## Acanthurus triostegus 0.82436 -0.56606 0.0530 0.039 *
## Chaetodon ephippium -0.66776 -0.74438 0.0369 0.086 .
## Chaetodon lunula -0.40693 -0.91346 0.0902 0.003 **
## Chaetodon ulietensis -0.31562 -0.94889 0.0187 0.256
## Epinephelus merra -0.74656 -0.66532 0.0354 0.074 .
## Zebrasoma scopas -0.68160 -0.73172 0.0458 0.039 *
## Carcharhinus melanopterus 0.61146 -0.79127 0.0379 0.075 .
## Labroides dimidiatus -0.01382 -0.99990 0.0810 0.009 **
## Scarus psittacus 0.08214 0.99662 0.2561 0.001 ***
## Balistapus undulatus -0.70752 -0.70669 0.0951 0.001 ***
## Centropyge flavissima -0.86580 0.50040 0.0126 0.371
## Chaetodon citrinellus -0.96244 0.27149 0.0195 0.243
## Cheilinus chlorourus -0.11225 0.99368 0.0996 0.001 ***
## Cheilinus trilobatus -0.87788 -0.47889 0.0804 0.005 **
## Naso lituratus 0.48849 0.87257 0.0033 0.764
## Cheilio inermis -0.44192 0.89706 0.0524 0.021 *
## Fistularia commersonii -0.12627 -0.99200 0.0182 0.263
## Cephalopholis argus 0.31679 -0.94850 0.0140 0.342
## Chaetodon auriga -0.41349 -0.91051 0.0473 0.032 *
## Chaetodon reticulatus 0.91588 -0.40144 0.0080 0.483
## Cheilinus undulatus 0.91588 -0.40144 0.0080 0.483
## Epibulus insidiator -0.79868 -0.60176 0.0199 0.248
## Siganus spinus -0.07211 0.99740 0.0981 0.003 **
## Chaetodon lunulatus -0.68043 -0.73281 0.0460 0.042 *
## Abudefduf sexfasciatus -0.97866 0.20548 0.0718 0.007 **
## Parupeneus insularis -0.02134 0.99977 0.0046 0.687
## Scarus forsteni 0.00341 -0.99999 0.0051 0.599
## Abudefduf septemfasciatus 0.11444 -0.99343 0.0117 0.382
## Hipposcarus longiceps 0.71159 -0.70259 0.0118 0.342
## Zanclus cornutus -0.70267 0.71152 0.0227 0.170
## Chrysiptera brownriggii -0.80674 -0.59090 0.0149 0.334
## Ostracion meleagris -0.17410 -0.98473 0.0011 0.910
## Pseudocheilinus evanidus -0.91894 -0.39441 0.0010 0.921
## Ellochelon vaigiensis 0.35252 0.93580 0.0015 0.846
## Chaetodon trifascialis -0.78812 0.61553 0.0012 0.907
## Himantura fai 0.99506 0.09929 0.0411 0.065 .
## Scarus oviceps -0.79114 -0.61163 0.0111 0.366
## Acanthurus guttatus 0.93590 -0.35226 0.0126 0.366
## Wrasse Unknown -0.16760 -0.98586 0.1164 0.002 **
## Chaetodon ornatissimus -0.66851 -0.74370 0.0109 0.353
## Oxycheilinus unifasciatus -0.65521 0.75545 0.0127 0.403
## Mulloidichthys vanicolensis -0.16291 0.98664 0.0704 0.018 *
## Aulostomus chinensis -0.26217 0.96502 0.0087 0.498
## Ostracion cubicus -0.14531 0.98939 0.0134 0.310
## Scarus ghobban -0.01587 0.99987 0.0273 0.150
## Ostorhinchus nigrofasciatus 0.23638 0.97166 0.0085 0.521
## Parrotfish Unknonwn -0.72674 0.68692 0.0148 0.309
## Lethrinus olivaceus 0.67531 0.73753 0.0013 0.916
## Parupeneus ciliatus -0.12848 0.99171 0.0184 0.228
## Lutjanus fulvus -0.98194 0.18919 0.0005 0.949
## Gymnothorax javanicus 0.29543 0.95536 0.0005 0.961
## Chromis viridis -0.77310 0.63429 0.1072 0.002 **
## Coris aygula -0.68096 0.73232 0.0133 0.326
## Stegastes fasciolatus 0.12913 0.99163 0.0245 0.170
## Stegastes albifasciatus -0.99948 -0.03213 0.0044 0.630
## Scorpaenopsis diabolus -0.96647 0.25677 0.0127 0.326
## Naso unicornis -0.14051 0.99008 0.0008 0.930
## Parupeneus barberinus 0.25195 0.96774 0.0672 0.019 *
## Dascyllus aruanus -0.33641 -0.94171 0.0776 0.011 *
## Kyphosus sp. 0.09359 -0.99561 0.0435 0.059 .
## Aluterus scriptus -0.10760 -0.99419 0.0069 0.555
## Arothron meleagris 0.19127 -0.98154 0.0219 0.193
## Cantherhines dumerilii 0.30426 -0.95259 0.0277 0.146
## Dascyllus trimaculatus -0.07705 0.99703 0.0001 0.992
## Labroides bicolor -0.30116 -0.95357 0.0093 0.385
## Crenimugil crenilabrus 0.73300 0.68023 0.1096 0.006 **
## Abudefduf sordidus -0.36430 0.93128 0.0117 0.369
## Bodianus perditio -0.13150 0.99132 0.0213 0.164
## Surgeonfish Unknown 0.44611 0.89498 0.0661 0.022 *
## Naso annulatus 0.27405 0.96171 0.0092 0.405
## Thalassoma purpureum 0.12725 -0.99187 0.0053 0.571
## Bothus spp 0.53748 0.84328 0.0456 0.052 .
## Cheilodipterus quinquelineatus 0.25326 0.96740 0.0685 0.022 *
## Pomacentrus pavo 0.29497 0.95551 0.0474 0.053 .
## Canthigaster bennetti 0.26213 0.96503 0.0341 0.100 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
##
## $factors
## NULL
##
## $na.action
## function (object, ...)
## UseMethod("na.action")
## <bytecode: 0x124edb378>
## <environment: namespace:stats>## NMDS1 NMDS2 Species
## Acanthurus nigrofuscus -0.19837200 0.10103982 Acanthurus nigrofuscus
## Caranx melampygus 0.03733804 0.03695211 Caranx melampygus
## Chaetodon vagabundus 0.49620144 0.09021222 Chaetodon vagabundus
## Chlorurus spilurus -0.06713940 0.03233486 Chlorurus spilurus
## Gomphosus varius -0.15652760 -0.39588358 Gomphosus varius
## Halichoeres trimaculatus -0.01946544 0.49251658 Halichoeres trimaculatus
## NMDS1 NMDS2
## Acanthurus nigrofuscus -0.198371999 0.10103982
## Chaetodon vagabundus 0.496201443 0.09021222
## Gomphosus varius -0.156527600 -0.39588358
## Halichoeres trimaculatus -0.019465438 0.49251658
## Rhinecanthus aculeatus -0.121229047 0.46826715
## Stegastes sp -0.254593596 -0.60259502
## Stethojulis bandanensis 0.004595799 0.48336150
## Thalassoma hardwicke -0.216496972 -0.22772575
## Acanthurus triostegus 0.189720909 -0.13027406
## Chaetodon lunula -0.122234035 -0.27438600
## Zebrasoma scopas -0.145855223 -0.15657993
## Labroides dimidiatus -0.003933122 -0.28456909
## Scarus psittacus 0.041567022 0.50432758
## Balistapus undulatus -0.218148044 -0.21789207
## Cheilinus chlorourus -0.035426058 0.31359961
## Cheilinus trilobatus -0.248930538 -0.13579416
## Cheilio inermis -0.101141698 0.20531021
## Chaetodon auriga -0.089944345 -0.19805551
## Siganus spinus -0.022586984 0.31242772
## Chaetodon lunulatus -0.145867998 -0.15709592
## Abudefduf sexfasciatus -0.262267228 0.05506554
## Wrasse Unknown -0.057191844 -0.33641310
## Mulloidichthys vanicolensis -0.043235064 0.26185235
## Chromis viridis -0.253140146 0.20768902
## Parupeneus barberinus 0.065315654 0.25087714
## Dascyllus aruanus -0.093732141 -0.26238295
## Crenimugil crenilabrus 0.242717318 0.22524458
## Surgeonfish Unknown 0.114726304 0.23016410
## Cheilodipterus quinquelineatus 0.066277050 0.25315979
## Species pval
## Acanthurus nigrofuscus Acanthurus nigrofuscus 0.032
## Chaetodon vagabundus Chaetodon vagabundus 0.001
## Gomphosus varius Gomphosus varius 0.001
## Halichoeres trimaculatus Halichoeres trimaculatus 0.001
## Rhinecanthus aculeatus Rhinecanthus aculeatus 0.001
## Stegastes sp Stegastes sp 0.001
## Stethojulis bandanensis Stethojulis bandanensis 0.001
## Thalassoma hardwicke Thalassoma hardwicke 0.002
## Acanthurus triostegus Acanthurus triostegus 0.039
## Chaetodon lunula Chaetodon lunula 0.003
## Zebrasoma scopas Zebrasoma scopas 0.039
## Labroides dimidiatus Labroides dimidiatus 0.009
## Scarus psittacus Scarus psittacus 0.001
## Balistapus undulatus Balistapus undulatus 0.001
## Cheilinus chlorourus Cheilinus chlorourus 0.001
## Cheilinus trilobatus Cheilinus trilobatus 0.005
## Cheilio inermis Cheilio inermis 0.021
## Chaetodon auriga Chaetodon auriga 0.032
## Siganus spinus Siganus spinus 0.003
## Chaetodon lunulatus Chaetodon lunulatus 0.042
## Abudefduf sexfasciatus Abudefduf sexfasciatus 0.007
## Wrasse Unknown Wrasse Unknown 0.002
## Mulloidichthys vanicolensis Mulloidichthys vanicolensis 0.018
## Chromis viridis Chromis viridis 0.002
## Parupeneus barberinus Parupeneus barberinus 0.019
## Dascyllus aruanus Dascyllus aruanus 0.011
## Crenimugil crenilabrus Crenimugil crenilabrus 0.006
## Surgeonfish Unknown Surgeonfish Unknown 0.022
## Cheilodipterus quinquelineatus Cheilodipterus quinquelineatus 0.022
Constrained Ordination - Distance based redundancy analysis
I started with using the pulse pc axis, the low tide mean silicate, the distance to seep, and the distance to shore:
Tests revealed the shore distance didn’t matter (permutation test p = 0.555), so I changed this to only use the pulse pc axis, the low tide mean silicate, the distance to seep, and the distance to shore:
## Call: capscale(formula = transformed_communities ~
## Low_Tide_Mean_Phosphate_umolL + Low_Tide_Mean_Silicate_umolL +
## dist_to_seep_m + Low_Tide_Mean_NN_umolL + Low_Tide_Mean_Ammonia_umolL,
## data = transformed_communities_with_explan, distance = "bray")
##
## Inertia Proportion Rank
## Total 32.6895
## RealTotal 41.2293 1.0000
## Constrained 6.0008 0.1455 5
## Unconstrained 35.2284 0.8545 59
## Imaginary -8.5398
## Inertia is squared Bray distance
## Species scores projected from 'transformed_communities'
##
## Eigenvalues for constrained axes:
## CAP1 CAP2 CAP3 CAP4 CAP5
## 2.8639 1.7815 0.7000 0.3982 0.2572
##
## Eigenvalues for unconstrained axes:
## MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
## 4.143 3.453 2.448 2.065 1.891 1.814 1.428 1.308
## (Showing 8 of 59 unconstrained eigenvalues)The output reports the total inertia, which is the total amount of variation (dissimilarity) in the data. This inertia is decomposed into ‘constrained’ and ‘unconstrained’ components. The constrained component is the total amount of variation explained by the predictors (18.36%), while the unconstrained component is the remaining ‘residual’ variation. There is also info on ‘real’ and ‘imaginary’ components, due to the negative eigenvalues issue which arises with PCoA.
The default plot shows how these variables are loaded onto the first
two CAP axes, and shows how the samples (circles) are ordinated on those
axes, as well as the species scores (red crosses).
We can get the variance explained by these axes from summary:
## $importance
## Importance of components:
## CAP1 CAP2 CAP3 CAP4 CAP5 MDS1 MDS2
## Eigenvalue 2.86392 1.78146 0.70002 0.398223 0.257219 4.1435 3.45276
## Proportion Explained 0.06946 0.04321 0.01698 0.009659 0.006239 0.1005 0.08375
## Cumulative Proportion 0.06946 0.11267 0.12965 0.139309 0.145548 0.2460 0.32979
## MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 MDS9
## Eigenvalue 2.44768 2.06495 1.89082 1.81446 1.42821 1.30773 1.15768
## Proportion Explained 0.05937 0.05008 0.04586 0.04401 0.03464 0.03172 0.02808
## Cumulative Proportion 0.38916 0.43924 0.48510 0.52911 0.56375 0.59547 0.62355
## MDS10 MDS11 MDS12 MDS13 MDS14 MDS15 MDS16
## Eigenvalue 1.08163 0.99460 0.93305 0.88491 0.81519 0.77587 0.71232
## Proportion Explained 0.02623 0.02412 0.02263 0.02146 0.01977 0.01882 0.01728
## Cumulative Proportion 0.64979 0.67391 0.69654 0.71800 0.73778 0.75659 0.77387
## MDS17 MDS18 MDS19 MDS20 MDS21 MDS22 MDS23
## Eigenvalue 0.68288 0.66182 0.60958 0.57898 0.54972 0.51744 0.47958
## Proportion Explained 0.01656 0.01605 0.01479 0.01404 0.01333 0.01255 0.01163
## Cumulative Proportion 0.79043 0.80649 0.82127 0.83531 0.84865 0.86120 0.87283
## MDS24 MDS25 MDS26 MDS27 MDS28 MDS29
## Eigenvalue 0.44805 0.39662 0.356002 0.3463 0.316808 0.296774
## Proportion Explained 0.01087 0.00962 0.008635 0.0084 0.007684 0.007198
## Cumulative Proportion 0.88370 0.89332 0.901952 0.9104 0.918036 0.925235
## MDS30 MDS31 MDS32 MDS33 MDS34 MDS35
## Eigenvalue 0.272894 0.253905 0.245929 0.220898 0.207260 0.1979
## Proportion Explained 0.006619 0.006158 0.005965 0.005358 0.005027 0.0048
## Cumulative Proportion 0.931854 0.938012 0.943977 0.949335 0.954362 0.9592
## MDS36 MDS37 MDS38 MDS39 MDS40 MDS41
## Eigenvalue 0.190279 0.168726 0.153957 0.147347 0.117402 0.11257
## Proportion Explained 0.004615 0.004092 0.003734 0.003574 0.002848 0.00273
## Cumulative Proportion 0.963777 0.967869 0.971604 0.975177 0.978025 0.98076
## MDS42 MDS43 MDS44 MDS45 MDS46 MDS47
## Eigenvalue 0.108468 0.096650 0.084325 0.074807 0.068334 0.059871
## Proportion Explained 0.002631 0.002344 0.002045 0.001814 0.001657 0.001452
## Cumulative Proportion 0.983386 0.985730 0.987775 0.989590 0.991247 0.992699
## MDS48 MDS49 MDS50 MDS51 MDS52 MDS53
## Eigenvalue 0.053540 0.049627 0.0399985 0.0370392 0.0317007 0.0244310
## Proportion Explained 0.001299 0.001204 0.0009701 0.0008984 0.0007689 0.0005926
## Cumulative Proportion 0.993998 0.995202 0.9961719 0.9970703 0.9978392 0.9984317
## MDS54 MDS55 MDS56 MDS57 MDS58
## Eigenvalue 0.0221729 0.0154990 0.013855 0.0077404 0.0046707
## Proportion Explained 0.0005378 0.0003759 0.000336 0.0001877 0.0001133
## Cumulative Proportion 0.9989695 0.9993454 0.999681 0.9998692 0.9999825
## MDS59
## Eigenvalue 0.0007216
## Proportion Explained 0.0000175
## Cumulative Proportion 1.0000000The first CAP axis explains 6.9% of the constrained community variation, and the second axis explains 4.3%. Therefore, this represents 2.07% of the total community variation.
Though this is already pretty poor, I might as well finish it out as a coding exercise. The loading coefficients for the explanatory variables on the constrained axes are:
## CAP1 CAP2 CAP3 CAP4
## Low_Tide_Mean_Phosphate_umolL 0.1369525 0.10403921 0.02676072 0.86815270
## Low_Tide_Mean_Silicate_umolL 0.1360874 -0.07483653 -0.47529639 0.84540902
## dist_to_seep_m -0.9235355 0.21983253 -0.16222694 -0.03515323
## Low_Tide_Mean_NN_umolL -0.3909944 0.09627340 -0.35963105 0.81929416
## Low_Tide_Mean_Ammonia_umolL -0.2597626 -0.59543126 0.32839866 0.53710311
## CAP5 MDS1
## Low_Tide_Mean_Phosphate_umolL 0.4647737 0
## Low_Tide_Mean_Silicate_umolL 0.1877675 0
## dist_to_seep_m 0.2668378 0
## Low_Tide_Mean_NN_umolL -0.1930737 0
## Low_Tide_Mean_Ammonia_umolL 0.4262154 0To test the important of all the predictors in combination:
## Permutation test for capscale under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: capscale(formula = transformed_communities ~ Low_Tide_Mean_Phosphate_umolL + Low_Tide_Mean_Silicate_umolL + dist_to_seep_m + Low_Tide_Mean_NN_umolL + Low_Tide_Mean_Ammonia_umolL, data = transformed_communities_with_explan, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 5 6.001 4.7014 0.001 ***
## Residual 138 35.228
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1So this tests whether the total variation explained by the constrained axes is significant. Interestingly, though the amount of variation seems trivial, it is showing up as significant…
## Permutation test for capscale under reduced model
## Forward tests for axes
## Permutation: free
## Number of permutations: 999
##
## Model: capscale(formula = transformed_communities ~ Low_Tide_Mean_Phosphate_umolL + Low_Tide_Mean_Silicate_umolL + dist_to_seep_m + Low_Tide_Mean_NN_umolL + Low_Tide_Mean_Ammonia_umolL, data = transformed_communities_with_explan, distance = "bray")
## Df SumOfSqs F Pr(>F)
## CAP1 1 2.864 11.2188 0.001 ***
## CAP2 1 1.781 6.9785 0.001 ***
## CAP3 1 0.700 2.7422 0.002 **
## CAP4 1 0.398 1.5600 0.144
## CAP5 1 0.257 1.0076 0.412
## Residual 138 35.228
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1So, both the axes we have plotted are considered significant.
## Permutation test for capscale under reduced model
## Marginal effects of terms
## Permutation: free
## Number of permutations: 999
##
## Model: capscale(formula = transformed_communities ~ Low_Tide_Mean_Phosphate_umolL + Low_Tide_Mean_Silicate_umolL + dist_to_seep_m + Low_Tide_Mean_NN_umolL + Low_Tide_Mean_Ammonia_umolL, data = transformed_communities_with_explan, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Low_Tide_Mean_Phosphate_umolL 1 1.228 4.8112 0.001 ***
## Low_Tide_Mean_Silicate_umolL 1 0.857 3.3569 0.001 ***
## dist_to_seep_m 1 1.055 4.1338 0.001 ***
## Low_Tide_Mean_NN_umolL 1 0.558 2.1855 0.006 **
## Low_Tide_Mean_Ammonia_umolL 1 1.737 6.8062 0.001 ***
## Residual 138 35.228
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Interestingly, all the axes are coming up as significant contributors to this variation.
PERMANOVA:
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = transformed_communities ~ Low_Tide_Mean_Phosphate_umolL + Low_Tide_Mean_Silicate_umolL + dist_to_seep_m + Low_Tide_Mean_NN_umolL + Low_Tide_Mean_Ammonia_umolL, data = transformed_communities_with_explan, by = "margin", dist = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Low_Tide_Mean_Phosphate_umolL 1 1.179 0.03608 6.0496 0.001 ***
## Low_Tide_Mean_Silicate_umolL 1 0.808 0.02472 4.1444 0.001 ***
## dist_to_seep_m 1 1.009 0.03087 5.1766 0.001 ***
## Low_Tide_Mean_NN_umolL 1 0.498 0.01524 2.5560 0.007 **
## Low_Tide_Mean_Ammonia_umolL 1 1.697 0.05192 8.7058 0.001 ***
## Residual 138 26.902 0.82296
## Total 143 32.690 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Again, all of our variables are significant contributors to community composition, though with very low r2.